Effect of Manipulation under Anesthesia of the First Knee in Staged Bilateral Total Knee Arthroplasty on Clinical Outcome and Satisfaction
Bibliographic record
Abstract
Abstract This study aimed to evaluate whether manipulation under anesthesia (MUA) affect clinical outcome including range of motion (ROM) and patient satisfaction after total knee arthroplasty (TKA). It is hypothesized that MUA improves clinical outcomes and patient satisfaction after primary TKA. This retrospective study analyzed 97 patients who underwent staged bilateral primary TKA. MUA of knee flexion more than 120 degrees was performed a week after index surgery just before operation of the opposite site. The first knees with MUA were classified as the MUA group and the second knees without MUA as the control group. ROM, Knee Society Knee Score, Knee Society Functional Score, Western Ontario and McMaster Universities (WOMAC) score, and patient satisfaction were assessed. Postoperative flexion was significantly greater in the MUA group during 6 months follow-up (6 weeks: 111.6 vs. 99.8 degrees, p < 0.001; 3 months: 115.9 vs. 110.2 degrees, p = 0.001; 6 months: 120.2 vs. 117.0 degrees, p = 0.019). Clinical outcomes also showed similar results with knee flexion during 2 years follow-up. Patient satisfaction was significantly high in the MUA group during 12 months (3 months: 80.2 vs. 71.5, p < 0.001; 6 months: 85.8 vs. 79.8, p < 0.001; 12 months: 86.1 vs. 83.9, p < 0.001; 24 months: 86.6 vs. 85.5, p = 0.013). MUA yielded improvement of clinical outcomes including ROM, and patient satisfaction, especially in the early period after TKA. MUA in the first knee could be taken into account to obtain early recovery and to improve patient satisfaction in staged bilateral TKA.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".